EEG changes associated with autistic spectrum disorders

KCNH5 deletion increases autism susceptibility by regulating neuronal growth through Akt/mTOR signaling pathway

Recent clinical studies have highlighted mutations in the voltage-gated potassium channel Kv10.2 encoded by the KCNH5 gene among individuals with autism spectrum disorder (ASD). Our preliminary study found that Kv10.2 was decreased in the hippocampus of valproic acid (VPA) - induced ASD rats. Nevertheless, it is currently unclear how KCNH5 regulates autism-like features, or becomes a new target for autism treatment. We employed KCNH5 knockout (KCNH5-/-) rats and VPA - induced ASD rats in this study. Then, we used behavioral assessments, combined with electrophysiological recordings and hippocampal brain slice, to elucidate the impact of KCNH5 deletion and environmental factors on neural development and function in rats. We found that KCNH5-/- rats showed early developmental delay, neuronal overdevelopment, and abnormal electroencephalogram (EEG) signals, but did not exhibit autism-like behavior. KCNH5-/- rats exposed to VPA (KCNH5-/--VPA) exhibit even more severe autism-like behaviors and abnormal neuronal development. The absence of KCNH5 excessively enhances the activity of the Protein Kinase B (Akt)/Mechanistic Target of Rapamycin (mTOR) signaling pathway in the hippocampus of rats after exposure to VPA. Overall, our findings underscore the deficiency of KCNH5 increases the susceptibility to autism under environmental exposures, suggesting its potential utility as a target for screening and diagnosis in ASD.

Magnetoencephalography for the pediatric population, indications, acquisition and interpretation for the clinician

Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.

Etiologic Evaluation of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is clinically and etiologically heterogeneous. A causal genetic variant can be identified in approximately 20% to 25% of affected individuals with current clinical genetic testing, and all patients with an ASD diagnosis should be offered genetic etiologic evaluation. We suggest that exome sequencing with copy number variant coverage should be the first-line etiologic evaluation for ASD. Neuroimaging, neurophysiologic, metabolic, and other biochemical evaluations can provide insight into the pathophysiology of ASD but should be recommended in the appropriate clinical circumstances.

Children with Autism Spectrum Disorder and Abnormalities of Clinical EEG: A Qualitative Review

Over the last decade, the comorbidity between Autism Spectrum Disorder (ASD) and epilepsy has been widely demonstrated, and many hypotheses regarding the common neurobiological bases of these disorders have been put forward. A variable, but significant, prevalence of abnormalities on electroencephalogram (EEG) has been documented in non-epileptic children with ASD; therefore, several scientific studies have recently tried to demonstrate the role of these abnormalities as a possible biomarker of altered neural connectivity in ASD individuals. This narrative review intends to summarize the main findings of the recent scientific literature regarding abnormalities detected with standard EEG in children/adolescents with idiopathic ASD. Research using three different databases (PubMed, Scopus and Google Scholar) was conducted, resulting in the selection of 10 original articles. Despite an important lack of studies on preschoolers and a deep heterogeneity in results, some authors speculated on a possible association between EEG abnormalities and ASD characteristics, in particular, the severity of symptoms. Although this correlation needs to be more strongly elucidated, these findings may encourage future studies aimed at demonstrating the role of electrical brain abnormalities as an early biomarker of neural circuit alterations in ASD, highlighting the potential diagnostic, prognostic and therapeutic value of EEG in this field.

An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG

Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns. In recent years, deep learning techniques have gained traction for early ASD detection. This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.

Ratings of the Effectiveness of 13 Therapeutic Diets for Autism Spectrum Disorder: Results of a National Survey

This study presents the results of the effectiveness of 13 therapeutic diets for autism spectrum disorder from 818 participants of a national survey, including benefits, adverse effects, and symptom improvements. The average Overall Benefit of diets was 2.36 (0 = no benefit, 4 = great benefit), which was substantially higher than for nutraceuticals (1.59/4.0) and psychiatric/seizure medications (1.39/4.0), p < 0.001. The average Overall Adverse Effects of diets was significantly lower than psychiatric/seizure medications (0.10 vs. 0.93, p < 0.001) and similar to nutraceuticals (0.16). Autism severity decreased slightly over time in participants who used diet vs. increasing slightly in those that did not (p < 0.001). Healthy and Feingold diets were the two top-rated diets by Overall Benefit; the ketogenic diet was the highest for nine symptoms (though had fewer respondents); and the gluten-free/casein-free diet was among the top for overall symptom improvements. Different diets were reported to affect different symptoms, suggesting that an individual's symptoms could be used to guide which diet(s) may be the most effective. The results suggest that therapeutic diets can be safe and effective interventions for improving some ASD-related symptoms with few adverse effects. We recommend therapeutic diets that include healthy foods and exclude problematic foods. Therapeutic diets are inexpensive treatments that we recommend for consideration by most people with ASD.

Social Brain Activation and Connectivity in Autism Spectrum Disorders: An Electroencephalogram Study of Jigsaw Puzzle Solving

Autism spectrum disorder requires early detection and treatment. Thus, we developed a method to obtain reliable neurophysiological biomarkers to assist in diagnosing autism. This method includes a simple but typical jigsaw puzzle that allows participants to play and interact with each other. While playing this game, brain signals of the participants were observed and analyzed. The patients with autism were found to have differences in the time range of some event-related potential, such as P300 and N400. Altered patterns of function connectivity were also found in delta frequency bands in the patients while interacting with other people. Working around patients' capabilities, the jigsaw puzzle game was designed as easy to complete; this caused fewer mismatch conditions. The result suggested that these patterns are promising neurophysiological biomarker to assist doctors in social cognitive assessment in autism.Clinical Relevance-This study demonstrated the possibility of using hyperscanning technique for social cognitive assessment of autism spectrum disorder.

Recurrence quantification analysis during a mental calculation task

The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time.

Evaluation of Branched-Chain Amino Acids in Children with Autism Spectrum Disorder and Epilepsy

Autism spectrum disorder (ASD) and epilepsy run hand-to-hand in their pathophysiology. Epilepsy is not an uncommon finding in patients with ASD. The aim of the present study was to identify the metabolic abnormalities of BCAAs (leucine, isoleucine, and valine) in children with ASD with and without seizures in comparison with neurotypical controls. Also, this study aimed to investigate the presence of epileptiform discharges on electroencephalography (EEG) in ASD patients and to describe the types and frequency of seizures observed. The study included 90 children aged 2-7 years, 30 of whom were diagnosed with both ASD and epilepsy. The other 30 children were diagnosed as ASD without epilepsy, and a comparable 30 normally developed children served as a control group. The groups were matched by age and gender. All patients were referred to the Autism Disorders Clinic for interviews and examinations. The Childhood Autism Rating Scale (CARS) was applied to all study participants to assess the degree of autism. The present study results show that all types of seizures may be identified in ASD children. The median serum levels of BCAAs were lower in ASD children with and without epilepsy than in neurotypical controls. This opens the door for discussion about new etiologies and better categorizations of ASD based on genotype and genetic abnormalities detected. More studies with larger samples are needed to understand ASD better and to more reliable evaluate the association between ASD, EEG changes, seizures, and BCAAs.

EEG Features in Autism Spectrum Disorder: A Retrospective Analysis in a Cohort of Preschool Children

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that can be associated with intellectual disability (ID) and epilepsy (E). The etiology and the pathogenesis of this disorder is in most cases still to be clarified. Several studies have underlined that the EEG recordings in children with these clinical pictures are abnormal, however the precise frequency of these abnormalities and their relationship with the pathogenic mechanisms and in particular with epileptic seizures are still unknown. We retrospectively reviewed 292 routine polysomnographic EEG tracings of preschool children (age < 6 years) who had received a first multidisciplinary diagnosis of ASD according to DSM-5 clinical criteria. Children (mean age: 34.6 months) were diagnosed at IRCCS E. Medea (Bosisio Parini, Italy). We evaluated: the background activity during wakefulness and sleep, the presence and the characteristics (focal or diffuse) of the slow-waves abnormalities and the interictal epileptiform discharges. In 78.0% of cases the EEG recordings were found to be abnormal, particularly during sleep. Paroxysmal slowing and epileptiform abnormalities were found in the 28.4% of the subjects, confirming the high percentage of abnormal polysomnographic EEG recordings in children with ASD. These alterations seem to be more correlated with the characteristics of the underlying pathology than with intellectual disability and epilepsy. In particular, we underline the possible significance of the prevalence of EEG abnormalities during sleep. Moreover, we analyzed the possibility that EEG data reduces the ASD clinical heterogeneity and suggests the exams to be carried out to clarify the etiology of the disorder.

Sensory Processing and Autistic Traits: Mediation Effect of Frontal Alpha Asymmetry

A sensory processing approach can be used to intervene with behaviours in individuals with autistic symptoms. However, neural mechanisms linking sensory processing patterns and autistic features are less understood. The purpose of this study was to investigate whether frontal alpha asymmetry could mediate the relationship between atypical sensory processing and autistic traits. Seventy-three neurotypical young adults were included in this study. Resting-state brain activity was recorded using electroencephalography. After the recording, participants completed the Adolescent/Adult Sensory Profile and the Autism-Spectrum Quotient. Frontal alpha asymmetry was calculated by subtracting left frontal alpha power from right frontal alpha power. Correlation analysis was performed to find which sensory processing patterns were related to frontal alpha asymmetry and autistic traits. Mediation analysis was then conducted with sensory avoiding patterns as an independent variable, autistic traits as a dependent variable, and frontal alpha asymmetry as a mediator. Interrelations between higher sensation avoiding patterns, greater right-sided cortical activity, and increased autistic traits were found. The sensation avoiding patterns affected autistic traits directly and indirectly through right-sided cortical activity. Findings of the current study demonstrate a mediating role of frontal alpha asymmetry in the relationship between sensation avoiding patterns and autistic traits in neurotypical adults. This study suggests that sensation avoiding patterns and withdrawal-related emotions, which are associated with right-sided cortical activity, need to be considered to improve autism symptoms.

Clinical EEG of Rett Syndrome: Group Analysis Supplemented with Longitudinal Case Report

Rett syndrome (RTT), a severe neurodevelopmental disorder caused by MECP2 gene abnormalities, is characterized by atypical EEG activity, and its detailed examination is lacking. We combined the comparison of one-time eyes open EEG resting state activity from 32 girls with RTT and their 41 typically developing peers (age 2-16 years old) with longitudinal following of one girl with RTT to reveal EEG parameters which correspond to the RTT progression. Traditional measures, such as epileptiform abnormalities, generalized background activity, beta activity and the sensorimotor rhythm, were supplemented by a new frequency rate index measured as the ratio between high- and low-frequency power of sensorimotor rhythm. Almost all studied EEG parameters differentiated the groups; however, only the elevated generalized background slowing and decrease in our newly introduced frequency rate index which reflects attenuation in the proportion of the upper band of sensorimotor rhythm in RTT showed significant relation with RTT progression both in longitudinal case and group analysis. Moreover, only this novel index was linked to the breathing irregularities RTT symptom. The percentage of epileptiform activity was unrelated to RTT severity, confirming previous studies. Thus, resting EEG can provide information about the pathophysiological changes caused by MECP2 abnormalities and disease progression.

Detection of Electroencephalographic Abnormalities and Its Associated Factors among Children with Autism Spectrum Disorder in Thailand

Epilepsy often causes more severe behavioral problems in children with autism spectrum disorder (ASD) and is strongly associated with poor cognitive functioning. Interestingly, individuals with ASD without a history of epilepsy can have abnormal electroencephalographic (EEG) activity. The aim of this study was to examine associations between EEG abnormalities and the ASD severity in children. The children with ASD who enrolled at the Rajanagarindra Institute of Child Development, Thailand were included in this study. The severity of ASD was measured by interviewing their parents with the Thai autism treatment evaluation checklist. The short sensory profile checklist was used for screening the abnormality of children in each domain. Ordinal logistic regression analysis was used to examine associations between factors potentially linked to EEG abnormalities. Most of the study participants were boys (87.5%) and the median age was 5 years. Among the 128 children, 69.5% showed EEG abnormalities (41.4% slow-wave and 28.1% epileptiform-discharge). The results show that a larger number of symptoms and increased severity of ASD were independently associated with a higher risk of EEG abnormalities. Our results emphasize the need for guidelines on the presence of EEG abnormalities in children with ASD for the early detection of epilepsy and improving treatment outcomes.

Emerging Electroencephalographic Biomarkers to Improve Preclinical to Clinical Translation in Alzheimer's Disease

Continually emerging data indicate that sub-clinical, non-convulsive epileptiform activity is not only prevalent in Alzheimer's disease (AD) but is detectable early in the course of the disease and predicts cognitive decline in both humans and animal models. Epileptiform activity and other electroencephalographic (EEG) measures may hold powerful, untapped potential to improve the translational validity of AD-related biomarkers in model animals ranging from mice, to rats, and non-human primates. In this review, we will focus on studies of epileptiform activity, EEG slowing, and theta-gamma coupling in preclinical models, with particular focus on its role in cognitive decline and relevance to AD. Here, each biomarker is described in the context of the contemporary literature and recent findings in AD relevant animal models are discussed.

Improvement of brain functional connectivity in autism spectrum disorder: an exploratory study on the potential use of virtual reality

Patients with Autism Spectrum Disorder (ASD) need to be provided with behavioral, psychological, educational, or skill-building interventions as early as possible. Cognitive Behavior Therapy has proven useful to manage such problems. There is also growing evidence on the usefulness of Virtual Reality Therapy (VRT) in treating various functional deficits in ASD. This exploratory study is aimed at assessing the changes in cognitive functions in children with ASD, and the putative subtending neurophysiological mechanisms, following the provision of rehab training using an innovative VRT system. Twenty patients with ASD, aged 6-15 years, were provided with 24 sessions of VRT by using the pediatric module of the BTS NIRVANA System. Neuropsychological and EEG evaluations were carried out before and at the end of the training. After VRT, all patients showed a significant improvement in their cognitive-behavioral problems concerning attention processes, visuospatial cognition, and anxiety. These findings were paralleled by an evident reshape of frontoparietal connectivity in the alpha and theta frequency range. Our study suggests that VRT could be a useful and promising tool to improve ASD neurorehabilitation outcomes. This improvement is likely to occur through changes in frontoparietal network connectivity following VRT.

Efficacy of neurofeedback as a treatment modality for children in the autistic spectrum

BACKGROUND

Neurofeedback (NFB) has been conceded as a convenient measure for both identifying and remodeling neural pliability of brain cells; it is a mean through which participants can have voluntary control on their brain waves being expressed on the EEG. Forty-two autistic children received a NFB therapy aiming at improving their cognitive abilities.

RESULTS

NFB succeeded to decrease children's high theta/beta ratio by inhibiting theta activity and intensifying beta activity over different sessions. Following therapy, the children's cognitive functions were found to show comparative improvement compared to pre-treatment assessment on a range of different tasks. Auxiliary improvements were found in their social, thought and attention domains.

CONCLUSION

These findings propose a basic cognitive function impairment in autism spectrum disorder that can be reduced through specific NFB treatment.

Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes

BACKGROUND

Autism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with autism.

METHODS

We used an FP-growth algorithm to retrospectively infer disease associations using 1488 patients with autism treated at the Guangzhou Women and Children's Medical Center. The disease network was established using Cytoscape 3.7. The rules were internally validated by 10-fold cross-validation. All rules were further verified using the Columbia Open Health Data (COHD) and by literature search.

RESULTS

We found 148 comorbid diseases including intellectual disability, developmental speech disorder, and epilepsy. The network comprised of 76 nodes and 178 directed links. 158 links were confirmed by literature search and 105 links were validated by COHD. Furthermore, we identified 14 links not previously reported.

CONCLUSION

We demonstrate that the FP-growth algorithm can detect comorbid disease patterns, including novel ones, in patients with autism.

Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals

Recent statistics have shown that the main difficulty in detecting alcoholism is the unreliability of the information presented by patients with alcoholism; this factor confusing the early diagnosis and it can reduce the effectiveness of treatment. However, electroencephalogram (EEG) exams can provide more reliable data for analysis of this behavior. This paper proposes a new approach for the automatic diagnosis of patients with alcoholism and introduces an analysis of the EEG signals from a two-dimensional perspective according to changes in the neural activity, highlighting the influence of high and low-frequency signals. This approach uses a two-dimensional feature extraction method, as well as the application of recent Computer Vision (CV) techniques, such as Transfer Learning with Convolutional Neural Networks (CNN). The methodology to evaluate our proposal used 21 combinations of the traditional classification methods and 84 combinations of recent CNN architectures used as feature extractors combined with the following classical classifiers: Gaussian Naive Bayes, K-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM). CNN MobileNet combined with SVM achieved the best results in Accuracy (95.33%), Precision (95.68%), F1-Score (95.24%), and Recall (95.00%). This combination outperformed the traditional methods by up to 8%. Thus, this approach is applicable as a classification stage for computer-aided diagnoses, useful for the triage of patients, and clinical support for the early diagnosis of this disease.

Investigating electroencephalography signals of autism spectrum disorder (ASD) using Higuchi Fractal Dimension

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a deficit of social relationships, interaction, sense of imagination, and constrained interests. Early diagnosis of ASD will aid in devising appropriate training procedures and placing those children in the normal stream. The objective of this research is to analyze the brain response for auditory/visual stimuli in Typically Developing (TD) and children with autism through electroencephalography (EEG). Brain dynamics in the EEG signal can be analyzed well with the help of nonlinear feature primitives. Recent research reveals that, application of fractal-based techniques proves to be effective to estimate of degree of nonlinearity in a signal. This research attempts to analyze the effect of brain dynamics with Higuchi Fractal Dimension (HFD). Also, the performance of the fractal based techniques depends on the selection of proper hyper-parameters involved in it. One of the key parameters involved in computation of HFD is the time interval parameter 'k'. Most of the researches arbitrarily fixes the value of 'k' in the range of all channels. This research proposes an algorithm to estimate the optimal value of the time parameter for each channel. Sub-band analysis was also carried out for the responding channels. Statistical analysis on the experimental reveals that a difference of 30% was observed between autistic and Typically Developing children.

Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics

Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network's activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.

Pathogenetical and Neurophysiological Features of Patients with Autism Spectrum Disorder: Phenomena and Diagnoses

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is accompanied by social deficits, repetitive and restricted interests, and altered brain development. The majority of ASD patients suffer not only from ASD itself but also from its neuropsychiatric comorbidities. Alterations in brain structure, synaptic development, and misregulation of neuroinflammation are considered risk factors for ASD and neuropsychiatric comorbidities. Electroencephalography has been developed to quantitatively explore effects of these neuronal changes of the brain in ASD. The pineal neurohormone melatonin is able to contribute to neural development. Also, this hormone has an inflammation-regulatory role and acts as a circadian key regulator to normalize sleep. These functions of melatonin may play crucial roles in the alleviation of ASD and its neuropsychiatric comorbidities. In this context, this article focuses on the presumable role of melatonin and suggests that this hormone could be a therapeutic agent for ASD and its related neuropsychiatric disorders.

Stem cell models of human synapse development and degeneration

Many brain disorders exhibit altered synapse formation in development or synapse loss with age. To understand the complexities of human synapse development and degeneration, scientists now engineer neurons and brain organoids from human-induced pluripotent stem cells (hIPSC). These hIPSC-derived brain models develop both excitatory and inhibitory synapses and functional synaptic activity. In this review, we address the ability of hIPSC-derived brain models to recapitulate synapse development and insights gained into the molecular mechanisms underlying synaptic alterations in neuronal disorders. We also discuss the potential for more accurate human brain models to advance our understanding of synapse development, degeneration, and therapeutic responses.

Delayed cortical maturation at the centrotemporal brain regions in patients with benign childhood epilepsy with centrotemporal spikes (BCECTS)

Benign childhood epilepsy with centrotemporal spikes (BCECTS) is an epilepsy syndrome commonly found in child and adolescent. Although the prognosis is mostly favorable as long as the seizure is well controlled. However, they are often suffering from the cognitive and behavioral problems which might be the consequences of the initial insults. It is still not clear whether the initial epileptiform discharges has long term impact on the resting-state brain activities at later ages. This study investigated the resting-state brain activities in BCECTS patients with clinical seizure remission stage (n = 16; 11 males) and compared with the non-epileptic, age-matched control subjects. Quantitative electroencephalography (qEEG) revealed a significantly higher absolute power of the theta and alpha waves in BCECTS patients with clinical seizure remission as compared with the non-epileptic control subjects. Interestingly, the differences were observed mainly over the centrotemporal electrodes which are the common sites of the initial epileptiform discharges. The differences were more significant in patients with bilateral epileptiform discharges than those with the unilateral epileptic activities. Typically, the brain wave power continuously decreases with increasing ages. Therefore, higher absolute powers of the brain waves indicate more delayed in cortical maturation compared with the non-epileptic control group. These findings indicated that BCECTS patients have delay cortical maturation at the centrotemporal brain regions even at the clinical seizure remission phase.

Multiplexed oscillations and phase rate coding in the basal forebrain

Complex behaviors demand temporal coordination among functionally distinct brain regions. The basal forebrain's afferent and efferent structure suggests a capacity for mediating this coordination at a large scale. During performance of a spatial orientation task, synaptic activity in this region was dominated by four amplitude-independent oscillations temporally organized by the phase of the slowest, a theta-frequency rhythm. Oscillation amplitudes were also organized by task epoch and positively correlated to the task-related modulation of individual neuron firing rates. For many neurons, spiking was temporally organized through phase precession against theta band field potential oscillations. Theta phase precession advanced in parallel to task progression, rather than absolute spatial location or time. Together, the findings reveal a process by which associative brain regions can integrate independent oscillatory inputs and transform them into sequence-specific, rate-coded outputs that are adaptive to the pace with which organisms interact with their environment.

Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

OBJECTIVE

Abnormal local and long-range brain connectivity have been widely reported in autism spectrum disorder (ASD), yet the nature of these abnormalities and their functional relevance at distinct cortical rhythms remains unknown. Investigations of intrinsic connectivity networks (ICNs) and their coherence across whole brain networks hold promise for determining whether patterns of functional connectivity abnormalities vary across frequencies and networks in ASD. In the present study, we aimed to probe atypical intrinsic brain connectivity networks in ASD from resting-state electroencephalography (EEG) data via characterizing the whole brain network.

APPROACH

Connectivity within individual ICNs (measured by spectral power) and between ICNs (measured by coherence) were examined at four canonical frequency bands via a time-frequency independent component analysis on high-density EEG, which were recorded from 20 ASD and 20 typical developing (TD) subjects during an eyes-closed resting state.

MAIN RESULTS

Among twelve identified electrophysiological ICNs, individuals with ASD showed hyper-connectivity in individual ICNs and hypo-connectivity between ICNs. Functional connectivity alterations in ASD were more severe in the frontal lobe and the default mode network (DMN) and at low frequency bands. These functional connectivity measures also showed abnormal age-related associations in ICNs related to frontal, temporal and motor regions in ASD.

SIGNIFICANCE

Our findings suggest that ASD is characterized by the opposite directions of abnormalities (i.e. hypo- and hyper-connectivity) in the hierarchical structure of the whole brain network, with more impairments in the frontal lobe and the DMN at low frequency bands, which are critical for top-down control of sensory systems, as well as for both cognition and social skills.

[Perspectives for the Electroencephalogram in Psychiatry]

Perspectives for the Electroencephalogram in Psychiatry Abstract. The electroencephalogram (EEG) is a non-invasive and cost-effective method to monitor spontaneous neuronal activity over time. Pathologies in EEG recordings indicate with high sensitivity but low specificity abnormal functional brain states. The main psychiatric indications for EEG recordings include atypical clinical symptoms of a neuropsychiatric syndrome or atypical reactions to medication as well as a baseline diagnostic before starting treatment with specific drugs or stimulation modalities. In recent research the EEG continues to be a valuable tool not only in diagnostics but also for the prediction of treatment success. The following paper focuses on basic electrophysiological understanding of EEG recordings, the diagnostic value of EEG recordings in different clinical entities, and new research attempts in diagnostic and treatment prediction.

The forsaking of the clinical EEG by psychiatry: how justified?

Despite decades of publications attesting to the role of the clinical EEG in diagnosing and managing psychiatric disorders, the procedure remains highly underutilized in the practice of psychiatry. The visually inspected EEG (vEEG) can detect various forms of abnormalities, each with its own clinical significance. Abnormalities can be paroxysmal (i.e., suggestive of an epileptic-like process) or stationary. The most important unanswered question remains the value of detecting epileptiform activity in a nonepileptic psychiatric patient in predicting favorable responses to anticonvulsant treatment. Despite the many shortcomings of vEEG, the available evidence suggests that in the presence of paroxysmal activity in a nonepileptic psychiatric patient a trial of a psychotropic anticonvulsant may be warranted if standard treatment has failed. More research on the contribution of paroxysmal EEG abnormalities to the problem of episodic psychiatric symptoms (e.g., panic attacks, dissociative episodes, repeated violence) is sorely needed. It is postulated that at least some of these conditions may represent an epilepsy spectrum disorder. Similarly, the significance of the presence of a slow-wave activity (whether focal or generalized) also deserves further well-designed research to ascertain the exact clinical significance. Nonetheless, the available data suggest that further medical workup is necessary to ascertain the nature and degree of the pathology when present.

Frequency of risk factors and coexisting abnormalities in a population of Egyptian children with autism spectrum disorder

BACKGROUND

Many risk factors interact together during the critical period of development and govern the future phenotype of autism spectrum disorder (ASD). Furthermore, co-occurring abnormalities among individuals with ASD vary a lot so as their abilities.

AIM OF WORK

To investigate possible risk factors and to determine the prevalence of coexisting abnormalities in a sample of Egyptian ASD children and their influence on the severity and their communication performance.

METHODS

The diagnosis and severity of ASD for participants (N=80) was performed by DSM-5, ADIR and CARS. They were investigated regarding the possible risk factors and coexisting abnormalities. A detailed history taking, clinical examination, the Arabic preschool language scale, cognitive abilities assessment and other additional instrumental measures such as EEG were used.

RESULTS

Caesarian section and neonatal jaundice were the most common risk factors. The severity of ASD was positively related to maternal and paternal ages. Developmental language disorder, intellectual disability, attention deficit hyperactivity disorder, sleep disorder and EEG changes were more frequently detected among studied cases. The CARS scores were significantly higher in ADHD and EEG changes groups. The most severely affected CARS items in the groups with these disorders were determined.

CONCLUSION

High parental ages has an impact on the severity of ASD. ADHD, sleep disorder, and EEG changes seem to have an impact on certain elements of the adaptive behavior especially the communicative performance of ASD individuals. We recommend to seriously investigate co-morbid abnormalities and consider them during the process of management of ASD for proper intervention plans.

Detection of time-, frequency- and direction-resolved communication within brain networks

Electroencephalography (EEG) records fast-changing neuronal signalling and communication and thus can offer a deep understanding of cognitive processes. However, traditional data analyses which employ the Fast-Fourier Transform (FFT) have been of limited use as they do not allow time- and frequency-resolved tracking of brain activity and detection of directional connectivity. Here, we applied advanced qEEG tools using autoregressive (AR) modelling, alongside traditional approaches, to murine data sets from common research scenarios: (a) the effect of age on resting EEG; (b) drug actions on non-rapid eye movement (NREM) sleep EEG (pharmaco-EEG); and (c) dynamic EEG profiles during correct vs incorrect spontaneous alternation responses in the Y-maze. AR analyses of short data strips reliably detected age- and drug-induced spectral EEG changes, while renormalized partial directed coherence (rPDC) reported direction- and time-resolved connectivity dynamics in mice. Our approach allows for the first time inference of behaviour- and stage-dependent data in a time- and frequency-resolved manner, and offers insights into brain networks that underlie working memory processing beyond what can be achieved with traditional methods.

High-Frequency EEG Variations in Children with Autism Spectrum Disorder during Human Faces Visualization

Autism spectrum disorder (ASD) is a neuropsychiatric disorder characterized by the impairment in the social reciprocity, interaction/language, and behavior, with stereotypes and signs of sensory function deficits. Electroencephalography (EEG) is a well-established and noninvasive tool for neurophysiological characterization and monitoring of the brain electrical activity, able to identify abnormalities related to frequency range, connectivity, and lateralization of brain functions. This research aims to evidence quantitative differences in the frequency spectrum pattern between EEG signals of children with and without ASD during visualization of human faces in three different expressions: neutral, happy, and angry. Quantitative clinical evaluations, neuropsychological evaluation, and EEG of children with and without ASD were analyzed paired by age and gender. The results showed stronger activation in higher frequencies (above 30 Hz) in frontal, central, parietal, and occipital regions in the ASD group. This pattern of activation may correlate with developmental characteristics in the children with ASD.

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis.

Epilepsy spectrum disorders: A concept in need of validation or refutation

Episodic psychiatric symptoms are not uncommon and range from panic attacks to repeated violent acts. Some evidence has accumulated over the years that at least in a subset of patients exhibiting these symptoms there may be evidence for the presence of focal cortical/subcortical hyperexcitability. In these cases the condition could be conceptualized as an epilepsy spectrum disorder (ESD) with significant treatment implications. There is currently no clear demarcation of this category of symptoms, their prevalence, an understanding of how these symptoms occur, what is appropriate work up and possible treatments. In this article, we propose that milder degrees of increased neural excitability (i.e., a subthreshold excitation insufficient to cause seizures) may nonetheless be capable of causing observable phenotypic changes. The observable phenotypic changes depend on the degree of hyperexcitability and the location of the hyperexcitable neural tissue. The location of the abnormal neural tissue may dictate the initial manifestation of an attack resulting from activation of the hyperexcitable tissue, but the anatomical connectivity of the abnormal region will dictate the breadth of manifestations. We provide some evidence, derived mainly from either electroencephalography studies of these populations or clinical reports of response to anti-epilepsy treatment, for the assumption and propose methods to test the advanced hypothesis.